With the proliferation of the Internet, more and more users are accessing more and more information online. Often, the same type of information is available from multiple online sources, each with markedly different utility and reliability. As the scope and volume of our electronic interactions continues to grow, there is a greater need for systems and methods for ranking online sources, so that users may quickly find the most useful and reliable sources of the information they are seeking.
Conventional search engines use link analysis algorithms, such as Hyperlink-Induced Topic Search (HITS) algorithms to rank search results. Starting from a simple keyword search, a HITS algorithm identifies “hubs” and “authorities” related to the search string by assigning each page an authority weight and a hub weight. “Authorities” are webpages that contain authoritative information on the search topic. “Hubs” are pages that point or link to these authorities. The authority weight and hub weight are defined recursively: a higher authority weight is given to pages that are pointed to by many pages with high hub weights, while a higher hub weight is given to pages that point to many pages with high authority weights. By recursively following the links between webpages, the HITS algorithm can identify the “most authoritative” webpages on a given topic, even if those webpages do not themselves include the initial search string.
Similarly, with more and more transactions being conducted online, there is also a greater need for systems and methods for accurately ranking vendors in online marketplaces. In such marketplaces, the same goods may be made available for purchase online from different vendors at different prices. In addition to these price differences, however, the vendors themselves may vary in trustworthiness and reliability. For example, some vendors may fail to deliver the goods by the promised date or otherwise fail to provide good customer service. Some vendors may deliver goods that are in poor condition, or past their expiration date. And some vendors may misuse credit card information that is entrusted to them. As a result of these types of irregularities, some vendors may develop a reputation as unreliable, untrustworthy, and unworthy of a customer's business.
In online or electronic commerce (e-commerce) marketplaces, an individual vendor's reputation is often expressed in terms of, for example, a star rating (e.g., from one to five stars), a letter grade, or the percent of positive or negative reviews that buyers have provided to ratings websites or online marketplaces regarding that vendor. However, such ratings are often subjective and open to interpretation. Moreover, because not all buyers will provide a rating or review (whether positive or negative), existing rating systems cannot provide a complete picture of a vendor's reputation.
Existing rating systems are also subject to manipulation. For example, the HITS algorithm may be manipulated to a certain extent by so-called “link farms” designed to make a spam page appear to be authoritative on a given search topic by generating thousands of fake hubs linking the spam page to that topic. Similarly, existing vendor ratings pages may be manipulated by unscrupulous vendors submitting fake positive reviews of themselves—or fake negative of their competitors. Existing ratings systems thus raise questions regarding the trustworthiness and reliability of the methods used to provide the ratings or reviews, as well as the trustworthiness and reliability of the webpages and e-commerce vendors that are the subject of those reviews.
There is thus a need for ranking systems that are less subjective and subject to manipulation.